1. Econ201FS- New Jump Test
Haolan Cai

2. Lee and Mykland (2007)
Introduces a new Jump Test with New test statistic (ζ)
Describes Asymptotic Behavior of ζ
Describes Misclassifications of ζ
Becomes negligible at high frequencies
Tests jump detection using MCMC Simulation
Claims it detects jumps better than Barndoff-Nielsen and Shephard (2006) and Jiang and Oomen (2005)

3. My Contribution
Investigation of Jump Test Detection in real data (GE and Intel)
Comparison to existing BN-S tests at varying time intervals

4. What is ζ?
So basically, ζ is the proportion of logged returns to the realized bipower variation over a given window size, K. Claim that using bipower variation for estimating instantaneous volatility is robust.

5. How is ζ distributed?
Ui is a standard normal variable which means its distributed with a mean of zero and variance of 1.
C is a constant approximately equal to

6. What does K mean?
Lee & Mykland (2007)

7. Implementation Data: Test Parameters:
1-minute prices from General Electric and Intel Corporation
GE: April 9, :35 am to Jan 24, :59 pm
INTC: April 16, :35 am to Jan 24, :59 pm
Test Parameters:
K = 10 (for Lee & Mykland test)
Use Quad-power Quarticity (for BN-S test)
Time Intervals: 1-10 min.
Significance Level: .01% (z-score = 3.09)

8. Results- INTC (in days)
Barndoff-Nielsen & Shephard
Lee & Mykland

9. Results- INTC (in percentages)
Lee & Mykland
Barndoff-Nielsen & Shephard

10. Similar Results for GE
Barndoff-Nielsen & Shephard
Lee & Mykland

11. Remarks
Lee and Mykland test does seem to be less affected by increase in time interval
Consistently finds the same number/percentage of jump days
Are these jumps ‘real jumps’ or due to microstructure noise?
Are the jumps being detected by the L & M test the same as the BN-S test?